{"id":4533,"date":"2026-04-23T19:41:22","date_gmt":"2026-04-23T19:41:22","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2026\/04\/23\/openais-new-gpt-5-5-powers-codex-on-nvidia-infrastructure-and-nvidia-is-already-putting-it-to-work\/"},"modified":"2026-04-23T19:41:22","modified_gmt":"2026-04-23T19:41:22","slug":"openais-new-gpt-5-5-powers-codex-on-nvidia-infrastructure-and-nvidia-is-already-putting-it-to-work","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2026\/04\/23\/openais-new-gpt-5-5-powers-codex-on-nvidia-infrastructure-and-nvidia-is-already-putting-it-to-work\/","title":{"rendered":"OpenAI\u2019s New GPT-5.5 Powers Codex on NVIDIA Infrastructure \u2014 and NVIDIA Is Already Putting It to Work"},"content":{"rendered":"
\n

AI agents have revolutionized developer workflows, and their next frontier is knowledge work: processing information, solving complex problems, coming up with new ideas and driving innovation.\u00a0<\/span><\/p>\n

Codex, OpenAI\u2019s agentic coding application, is enabling this new frontier. It\u2019s now powered by GPT-5.5, OpenAI\u2019s latest frontier model, which runs on NVIDIA GB200 NVL72 rack-scale systems.\u00a0<\/span><\/p>\n

Over 10,000 NVIDIANs \u2014 across engineering, product, legal, marketing, finance, sales, HR, operations and developer programs \u2014 are already using GPT-5.5-powered Codex to achieve, in their words, \u201cmind-blowing\u201d and \u201clife-changing\u201d results.\u00a0<\/span><\/p>\n

NVIDIA engineers have had access to GPT-5.5 through the Codex app for a few weeks, and the gains are measurable. Served on GB200 NVL72, which is capable of delivering 35x lower cost per million tokens and 50x higher token output per second per megawatt compared with prior-generation systems \u2014 <\/span>economics<\/span><\/a> that make frontier-model inference viable at enterprise scale.<\/span><\/p>\n

Debugging cycles that once stretched across days are closing in hours. Experimentation that previously required weeks is turning into overnight progress in complex, multi-file codebases. Teams are shipping end-to-end features from natural-language prompts, with stronger reliability and fewer wasted cycles than earlier models.\u00a0<\/span><\/p>\n

OpenAI\u2019s stunning progress is just the latest example of NVIDIA\u2019s work with every frontier model company \u2014 not just to accelerate the use of AI agents inside NVIDIA, but to help the company\u2019s partners build the world\u2019s best, lowest cost and most power efficient models for everyone.<\/span><\/p>\n

As NVIDIA founder and CEO Jensen Huang told employees in a company-wide email urging everyone to use Codex: \u201cLet\u2019s jump to lightspeed. Welcome to the age of AI.\u201d<\/span><\/p>\n

A Deployment Built for Enterprise Security\u00a0<\/b><\/h2>\n

Just like humans, every agent needs its own dedicated computer.\u00a0<\/span><\/p>\n

To ensure seamless operation within secure enterprise environments, <\/span>the Codex app supports remote Secure Shell (SSH) connections to approved cloud virtual machines, allowing agents to work with real company data without exposing it externally.\u00a0<\/span><\/p>\n

So <\/span>to ensure maximum security and auditability, NVIDIA IT rolled out cloud virtual machines (VMs) for every employee to run their agent safely. This provides a dedicated sandbox for the agent to operate at its maximum capabilities while maintaining full auditability.\u00a0 Users can control the Codex agent running in the cloud VM from a user interface that every employee is familiar with.<\/span><\/p>\n

A zero-data retention policy governs NVIDIA\u2019s deployment, and agents access production systems with read-only permissions through command-line interfaces and Skills \u2014 the same agentic toolkit NVIDIA uses to run automation workflows across the company.<\/span><\/p>\n

\"\"<\/p>\n

A Decade of Full-Stack Collaboration<\/b><\/h2>\n

The GPT-5.5 launch and the Codex rollout reflect more than 10 years of collaboration between NVIDIA and OpenAI. The partnership began in 2016, when NVIDIA founder and CEO Jensen Huang hand-delivered the first NVIDIA DGX-1 AI supercomputer to OpenAI\u2019s San Francisco headquarters.<\/span><\/p>\n

Since then, the two companies have worked closely across the full AI stack.\u00a0<\/span><\/p>\n

NVIDIA was a day-zero partner for OpenAI\u2019s gpt-oss open-weight model launch, optimizing model weights for NVIDIA TensorRT-LLM and ecosystem frameworks including vLLM and Ollama.\u00a0<\/span><\/p>\n

OpenAI has committed to deploying more than 10 gigawatts of NVIDIA systems for its next-generation AI infrastructure \u2014 a buildout that will put millions of NVIDIA GPUs at the foundation of OpenAI\u2019s model training and inference for years ahead.<\/span><\/p>\n

And OpenAI and NVIDIA are early silicon and co-design partners: OpenAI provides feedback that informs NVIDIA\u2019s hardware roadmap, and in turn gains early access to new architectures. That relationship produced a concrete milestone \u2014 the joint bring-up of the first GB200 NVL72 100,000-GPU cluster. The cluster completed multiple large-scale training runs and set a new benchmark for system-level reliability at frontier scale.<\/span><\/p>\n

GPT-5.5 is the product of that infrastructure running at full strength.\u00a0<\/span><\/p>\n

Learn more in <\/span><\/i>OpenAI\u2019s announcement<\/span><\/i><\/a>. <\/span><\/i><\/p>\n<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"

https:\/\/blogs.nvidia.com\/blog\/openai-codex-gpt-5-5-ai-agents\/<\/p>\n","protected":false},"author":0,"featured_media":4534,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[3],"tags":[],"_links":{"self":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/4533"}],"collection":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/comments?post=4533"}],"version-history":[{"count":0,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/4533\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media\/4534"}],"wp:attachment":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media?parent=4533"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/categories?post=4533"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/tags?post=4533"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}